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A Monte Carlo method for in silico modeling and visualization of Waddington's epigenetic landscape with intermediate details | |
2020-12 | |
发表期刊 | BIOSYSTEMS (IF:2.0[JCR-2023],1.8[5-Year]) |
ISSN | 0303-2647 |
EISSN | 1872-8324 |
卷号 | 198页码:#VALUE! |
DOI | 10.1016/j.biosystems.2020.104275 |
摘要 | Waddington's epigenetic landscape is a classic metaphor for describing the cellular dynamics during the development modulated by gene regulation. Quantifying Waddington's epigenetic landscape by mathematical modeling would be useful for understanding the mechanisms of cell fate determination. A few computational methods have been proposed for quantitative modeling of landscape; however, to model and visualize the landscape of a high dimensional gene regulatory system with realistic details is still challenging. Here, we propose a Monte Carlo method for modeling the Waddington's epigenetic landscape of a gene regulatory network (GRN). The method estimates the probability distribution of cellular states by collecting a large number of time-course simulations with random initial conditions. By projecting all the trajectories into a 2-dimensional plane of dimensions i and j, we can approximately calculate the quasi-potential U(x(i), x(j), *) = -ln P(x(i,) x(j), *), where P(x(i), x(j), *) is the estimated probability of an equilibrium steady state or a non-equilibrium state. Compared to the state-of-the-art methods, our Monte Carlo method can quantify the global potential landscape (or emergence behavior) of GRN for a high dimensional system. The potential landscapes show that not only attractors represent stability, but the paths between attractors are also part of the stability or robustness of biological systems. We demonstrate the novelty and reliability of our method by plotting the potential landscapes of a few published models of GRN. |
关键词 | Waddington's epigenetic landscape Monte Carlo Gene regulatory network Dynamical systems |
URL | 查看原文 |
收录类别 | SCI ; SCIE |
语种 | 英语 |
WOS研究方向 | Life Sciences & Biomedicine - Other Topics ; Mathematical & Computational Biology |
WOS类目 | Biology ; Mathematical & Computational Biology |
WOS记录号 | WOS:000595260100012 |
出版者 | ELSEVIER SCI LTD |
WOS关键词 | STOCHASTIC SIMULATION ; POTENTIAL LANDSCAPE ; PLURIPOTENT ; FRAMEWORK ; DYNAMICS |
原始文献类型 | Article |
引用统计 | 正在获取...
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文献类型 | 期刊论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/125965 |
专题 | 信息科学与技术学院_硕士生 信息科学与技术学院_PI研究组_郑杰组 |
通讯作者 | Zheng, Jie |
作者单位 | 1.Nanyang Technol Univ, Sch Comp Sci & Engn, Biomed Informat Lab, Singapore 639798, Singapore; 2.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China |
通讯作者单位 | 信息科学与技术学院 |
推荐引用方式 GB/T 7714 | Zhang, Xiaomeng,Chong, Ket Hing,Zhu, Lin,et al. A Monte Carlo method for in silico modeling and visualization of Waddington's epigenetic landscape with intermediate details[J]. BIOSYSTEMS,2020,198:#VALUE!. |
APA | Zhang, Xiaomeng,Chong, Ket Hing,Zhu, Lin,&Zheng, Jie.(2020).A Monte Carlo method for in silico modeling and visualization of Waddington's epigenetic landscape with intermediate details.BIOSYSTEMS,198,#VALUE!. |
MLA | Zhang, Xiaomeng,et al."A Monte Carlo method for in silico modeling and visualization of Waddington's epigenetic landscape with intermediate details".BIOSYSTEMS 198(2020):#VALUE!. |
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